{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shap Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>CRIM</th>\n",
       "      <th>ZN</th>\n",
       "      <th>INDUS</th>\n",
       "      <th>CHAS</th>\n",
       "      <th>NOX</th>\n",
       "      <th>RM</th>\n",
       "      <th>AGE</th>\n",
       "      <th>DIS</th>\n",
       "      <th>RAD</th>\n",
       "      <th>TAX</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>B</th>\n",
       "      <th>LSTAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.00632</td>\n",
       "      <td>18.0</td>\n",
       "      <td>2.31</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.02731</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.02729</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.07</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.03237</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.06905</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from BorutaShap import BorutaShap, load_data\n",
    "\n",
    "X, y = load_data(data_type='regression')\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [11:03<00:00,  6.63s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 attributes confirmed important: ['B', 'RM', 'NOX', 'CRIM', 'LSTAT', 'PTRATIO', 'AGE', 'TAX', 'DIS']\n",
      "4 attributes confirmed unimportant: ['INDUS', 'ZN', 'CHAS', 'RAD']\n",
      "0 tentative attributes remains: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# no model selected default is Random Forest, if classification is False it is a Regression problem\n",
    "Feature_Selector = BorutaShap(importance_measure='shap',\n",
    "                              classification=False)\n",
    "\n",
    "Feature_Selector.fit(X=X, y=y, n_trials=100, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns Boxplot of features\n",
    "# I would recommend plotting Y axis on the Log Scale\n",
    "Feature_Selector.plot(X_size=12, figsize=(12,8),\n",
    "            y_scale='log', which_features='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>B</th>\n",
       "      <th>RM</th>\n",
       "      <th>NOX</th>\n",
       "      <th>CRIM</th>\n",
       "      <th>LSTAT</th>\n",
       "      <th>PTRATIO</th>\n",
       "      <th>AGE</th>\n",
       "      <th>TAX</th>\n",
       "      <th>DIS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>396.90</td>\n",
       "      <td>6.575</td>\n",
       "      <td>0.538</td>\n",
       "      <td>0.00632</td>\n",
       "      <td>4.98</td>\n",
       "      <td>15.3</td>\n",
       "      <td>65.2</td>\n",
       "      <td>296.0</td>\n",
       "      <td>4.0900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>396.90</td>\n",
       "      <td>6.421</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.02731</td>\n",
       "      <td>9.14</td>\n",
       "      <td>17.8</td>\n",
       "      <td>78.9</td>\n",
       "      <td>242.0</td>\n",
       "      <td>4.9671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>392.83</td>\n",
       "      <td>7.185</td>\n",
       "      <td>0.469</td>\n",
       "      <td>0.02729</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.8</td>\n",
       "      <td>61.1</td>\n",
       "      <td>242.0</td>\n",
       "      <td>4.9671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>394.63</td>\n",
       "      <td>6.998</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.03237</td>\n",
       "      <td>2.94</td>\n",
       "      <td>18.7</td>\n",
       "      <td>45.8</td>\n",
       "      <td>222.0</td>\n",
       "      <td>6.0622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>396.90</td>\n",
       "      <td>7.147</td>\n",
       "      <td>0.458</td>\n",
       "      <td>0.06905</td>\n",
       "      <td>5.33</td>\n",
       "      <td>18.7</td>\n",
       "      <td>54.2</td>\n",
       "      <td>222.0</td>\n",
       "      <td>6.0622</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        B     RM    NOX     CRIM  LSTAT  PTRATIO   AGE    TAX     DIS\n",
       "0  396.90  6.575  0.538  0.00632   4.98     15.3  65.2  296.0  4.0900\n",
       "1  396.90  6.421  0.469  0.02731   9.14     17.8  78.9  242.0  4.9671\n",
       "2  392.83  7.185  0.469  0.02729   4.03     17.8  61.1  242.0  4.9671\n",
       "3  394.63  6.998  0.458  0.03237   2.94     18.7  45.8  222.0  6.0622\n",
       "4  396.90  7.147  0.458  0.06905   5.33     18.7  54.2  222.0  6.0622"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subset = Feature_Selector.Subset()\n",
    "subset.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Gini Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:44<00:00,  2.23it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 attributes confirmed important: ['B', 'RM', 'NOX', 'CRIM', 'LSTAT', 'PTRATIO', 'AGE', 'TAX', 'DIS']\n",
      "4 attributes confirmed unimportant: ['INDUS', 'ZN', 'CHAS', 'RAD']\n",
      "0 tentative attributes remains: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# no model selected default is Random Forest, if classification is False it is a Regression problem\n",
    "Feature_Selector = BorutaShap(importance_measure='gini',\n",
    "                              classification=False)\n",
    "\n",
    "Feature_Selector.fit(X=X, y=y, n_trials=100, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns Boxplot of features\n",
    "# I would recommend plotting Y axis on the Log Scale\n",
    "Feature_Selector.plot(X_size=12, figsize=(12,8),\n",
    "            y_scale='log', which_features='all')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Shap Regression Sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [01:45<00:00,  1.05s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9 attributes confirmed important: ['B', 'RM', 'NOX', 'CRIM', 'LSTAT', 'PTRATIO', 'AGE', 'TAX', 'DIS']\n",
      "4 attributes confirmed unimportant: ['INDUS', 'ZN', 'CHAS', 'RAD']\n",
      "0 tentative attributes remains: []\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# no model selected default is Random Forest, if classification is False it is a Regression problem\n",
    "Feature_Selector = BorutaShap(importance_measure='shap',\n",
    "                              classification=False)\n",
    "\n",
    "Feature_Selector.fit(X=X, y=y, n_trials=100, random_state=0, sample=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns Boxplot of features\n",
    "# I would recommend plotting Y axis on the Log Scale\n",
    "Feature_Selector.plot(X_size=12, figsize=(12,8),\n",
    "            y_scale='log', which_features='all')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.7.6 64-bit ('test2': conda)",
   "language": "python",
   "name": "python37664bittest2conda430366e329e54cb68cfb37f49729e20f"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
